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Abstract:

The present invention relates generally to the field of diagnostic and
prognostic assays for heart disease. More particular, the present
invention provides an assay for diagnosing the presence or extent of
development of heart disease or its classification or state thereof. The
assay of the present invention is also useful in the stratification of a
subject with respect to a risk of developing heart disease. The assay of
the present invention is also capable of integration into pathology
architecture to provide a diagnostic and reporting system.

Claims:

1. An assay to stratify a subject as a vulnerable or non-vulnerable
subject with respect to plaques, the assay comprising determining the
levels of at least two lipid analytes selected from the list consisting
of: (i) one or more modified lipid analytes listed in Table 1; (ii) two
or more non-modified lipid analytes listed in Table 1; and/or (iii) two
or more lipid analytes wherein at least one is a modified lipid analyte
listed in Table 1 and at least one is a non-modified lipid analyte listed
in Table 1; wherein the level of an individual lipid analyte listed in
Table 1 is different between vulnerable subjects and non-vulnerable
subjects and wherein the level of the lipid analytes in the subject
relative to a control identifies the subject as being vulnerable or
non-vulnerable.

2. The assay of claim 1, comprising comparing the level of the at least
two lipid analytes in the subject to the respective levels of the same
lipid analytes in at least one control subject selected from a vulnerable
subject and a non-vulnerable subject, wherein a similarity in the
respective levels of the at least two lipid analytes between the subject
and the non-vulnerable subject identifies the subject as being
non-vulnerable, and wherein a similarity in the respective levels of the
at least two lipid analytes between the subject and the vulnerable
subject identifies the subject as being vulnerable.

3. The assay of claim 2, further comprising comparing the level of the at
least two lipid analytes in the subject to the respective levels of the
same lipid analytes in at least one normal subject, wherein a similarity
in the respective levels of the at least two lipid analytes between the
subject and the normal subject identifies the subject as being normal
with respect to plaques.

5. The assay of claim 1, further comprising determining the levels of at
least two lipid analytes selected from the list consisting of: (i) one or
more modified lipid analytes listed in Table 1; (ii) two or more
non-modified lipid analytes listed in Table 1; and/or (iii) two or more
lipid analytes wherein at least one is a modified lipid analyte listed in
Table 1 and at least one is a non-modified lipid analyte listed in Table
1; wherein the level of an individual lipid analyte listed in Table 1 is
different between normal subjects and heart disease subjects and wherein
the level of the lipid analytes in the subject relative to a control
identifies the subject as being a normal, subject or a heart disease
subject.

6. The assay of claim 5, comprising comparing the level of the at least
two lipid analytes in the subject to the respective levels of the same
lipid analytes in at least one control subject selected from a normal
subject and a heart disease subject, wherein a similarity in the
respective levels of the at least two lipid analytes between the subject
and the heart disease subject identifies the subject as having heart
disease, and wherein a similarity in the respective levels of the at
least two lipid analytes between the subject and the normal subject
identifies the subject as being normal with respect to heart disease.

8. The assay of claim 1, wherein the or each modified lipid analyte in
(i) is selected from a modified ceramide (modCER) and a modified
phosphatidylcholine (modPC).

9. The assay of claim 1, wherein the non-modified lipid analytes in (ii)
are selected from a dihexosylceramide (DHC), a sphingomyelin (SM), a
phosphatidyhnositol (PI), a lysophosphatidylcholine (LPC), a
phosphatidylcholine (PC), an alkylphosphatidylcholine (APC), a
cholesterol ester (CE), a diacylglycerol (DG) and a triacylglycerol (TG).

10. The assay of claim 1, wherein the or each modified lipid analyte in
(iii) is selected from a modified ceramide (modCER) and a modified
phosphatidylcholine (modPC) and the or each non-modified lipid in (iii)
is selected from a dihexosylceramide (DHC), a sphingomyelin (SM), a
phosphatidyhnositol (PI), a lysophosphatidylcholine (LPC), a
alkylphosphatidylcholine (APC), a cholesterol ester (CE), a
diacylglycerol (DG) and a triacylglycerol (TG).

13. The assay of claim 1, wherein the assayed levels of lipid analytes
are used in combination with one or more traditional risk factors
selected from age, sex, smoker, diabetes, hypertension, CAD family
history, BMI, total cholesterol, LDL, HDL, triglycerides, glucose and
hsCRP to thereby identify the subject as being vulnerable or
non-vulnerable.

14. An assay to stratify a subject with respect to heart disease, the
assay comprising determining the levels of at least two lipid analytes
selected from the list consisting of: (i) one or more modified lipid
analytes listed in Table 1; (ii) two or more non-modified lipid analytes
listed in Table 1, and/or (iii) two or more lipid analytes wherein at
least one is a modified lipid analyte listed in Table 1 and at least one
is a non-modified lipid analyte listed in Table 1; wherein the level of
an individual lipid analyte listed in Table 1 is different between normal
and heart disease subjects and wherein the level of the lipid analytes in
the subject relative to a control provides an indication of the presence
or absence of heart disease.

15. The assay of claim 14, comprising comparing the level of the at least
two lipid analytes in the subject to the respective levels of the same
lipid analytes in at least one control subject selected from a normal
subject and a heart disease subject, wherein a similarity in the
respective levels of the at least two lipid analytes between the subject
and the heart disease subject identifies the subject having heart
disease, and wherein a similarity in the respective levels of the at
least two lipid analytes between the subject and the normal subject
identifies the subject as a normal subject with respect to heart disease.

22. The assay of claim 14, wherein the assayed levels of lipid analytes
are used in combination with one or more traditional risk factors
selected from age, sex, smoker, diabetes, hypertension, CAD family
history, BMI, total cholesterol, LDL, HDL, triglycerides, glucose and
hsCRP to thereby identify the subject as being normal or having heart
disease.

Description:

FIELD

[0001] The present invention relates generally to the field of diagnostic
and prognostic assays for heart disease. More particularly, the present
invention provides an assay for diagnosing the presence or extent of
development of heart disease or its classification or state thereof. The
assay of the present invention is also useful in the stratification of a
subject with respect to a risk of developing heart disease. The assay of
the present invention is also capable of integration into pathology
architecture to provide a diagnostic and reporting system.

BACKGROUND

[0002] Bibliographic details of references provided in the subject
specification are listed at the end of the specification.

[0003] Reference to any prior art is not, and should not be taken as an
acknowledgment or any form of suggestion that this prior art forms part
of the common general knowledge in any country.

[0004] Atherosclerosis (AS) is the single most common cause of heart
disease and is the major contributor to the development of angina, heart
attacks and stroke. Despite the introduction of statin-based therapy to
reduce levels of plasma low density lipoprotein (LDL) cholesterol, the
epidemic of heart disease is claiming tens of thousands of lives each
year, particularly in Western countries and costs the health system over
billions of dollars per year (National Health Survey: Summary of Results,
Australia, 2004-05, cat. no. 4364.0, ABS, Can berra, Vol: Australian
Bureau of Statistics, 2006, (AIHW) AIoHaW. Health system expenditure on
disease and injury in Australia, 2000-01. Health and Welfare Expenditure
Series No. 19, 2004; HWE 26).

[0005] Atherosclerosis begins to develop early in life and progresses with
time. However, the rate of progression is, to a large extent,
unpredictable and differs markedly amongst seemingly comparable
individuals. One of the early events leading to Atherosclerosis is the
formation of "fatty streaks", deposits of monocytes, macrophages, foam
cells and lipids within the intima of the arterial wall. Fatty streaks
exist in most adults and can remain as fatty streaks for years or
decades, having little or no adverse clinical effects. Some, but not all,
fatty steaks progress into fibriolipid plaques which are distinguished by
the presence of smooth muscle cells and increased extracellular fibres
within the intima. Cell death within the plaque leads to the formation of
a necrotic core, the accumulation of extracellular material and the
formation of the complex plaque. At this stage, the plaque may severely
restrict blood flow leading to a range of clinical complications;
however, many individuals will be unaware of the problem and show no
symptoms.

[0006] Complex plaques can become unstable (a "vulnerable" plaque) as a
result of the thinning of the smooth muscle cell layer over the plaque.
Unstable plaques may rupture leading to thrombosis, myocardial infarction
and stroke with the associated morbidity and mortality (the "vulnerable"
patient). Although plaque accumulation and development is progressive
throughout life, the switch from stable to unstable plaque can occur
earlier or later in the disease process. Thus a 45 year old with
relatively low levels of plaque can become unstable leading to a coronary
event.

[0007] Despite our detailed knowledge of plaque pathology and progression
many individuals have no clinical symptoms and so are unaware of their
risk. In 30 to 50% of these individuals, the first indicator of
Atherosclerosis is an acute heart attack which is often fatal (Heart
Disease and Stroke Statistics-2006 Update, Dallas Tex.: American Heart
Association, 2006. Available at
http://www.americanheart.org/downloadable/heart/1198257493273HS_Stats%202-
008.pdf)

[0008] A non-invasive assay is required to identify and monitor heart
disease.

SUMMARY

[0009] Each embodiments described herein is to be applied mutatis mutandis
to each any every embodiment unless specifically stated otherwise.

[0010] The present invention applies a lipidomic approach to identifying
the presence, development, stage or severity of heart disease or its
various manifestations.

[0011] An association is therefore identified between the level of
lipidomic analytes in a subject and heart disease. The term "analyte"
includes biomarker and indicator. By "heart disease" is meant an
individual condition as well as a collection of conditions within the
clinical spectrum of symptomatic or asymptomatic heart disease. The
lipidomic biomarkers provide a range of risk indicators of the severity
of disease and rate of progression and a classification of the disease
such as stable or unstable in relation to plaques. This risk ranges from
minor to extreme. Knowledge of the level of risk enables intervention to
mitigate further development of heart disease. The ability to monitor and
identify markers of heart disease including diagnosing it in asymptomatic
subjects further enables decisions on the type of medical intervention
required from behavioural modification and medicaments to surgical
intervention. The lipidomic biomarkers are also instructive as to the
level of risk for an individual developing more severe symptomology
associated with heart disease. The lipidomic profile also defines a
desired state of health in subjects. Hence, monitoring changing levels of
lipid analytes is a useful tool in pharmacotranslational studies and
clinical management of patients.

[0013] The present invention is predicated in part on the determination
that subjects with heart disease or at risk of developing heart disease
exhibit altered lipid metabolism. The levels of particular lipidomic
analytes correlate with the state, stage and/or classification of heart
disease and its progression in symptomatic and asymptomatic subjects. By
"classification" includes identifying subjects with stable and unstable
plaques and hence, individuals can be classified as vulnerable or
non-vulnerable subjects. Hence, the present invention enables
stratification of subjects into risk categories, treatment categories and
likely progression outcomes.

[0014] Twenty-three different lipid classes and three hundred and
twenty-nine lipid analytes were analysed. Ten different lipid classes
comprising thirty lipid analytes were particularly useful for
distinguishing between vulnerable and non-vulnerable subjects. Further,
eighteen lipid classes comprising ninety-five lipid analytes were useful
for distinguishing between control normal subjects and subjects with
coronary artery disease. Furthermore, as summarised in Table 16,
phosphatidylinositol lipids including seventeen lipid analytes in this
class were on average significantly reduced in vulnerable subjects;
thirteen lipid classes were reduced on average in coronary artery disease
subjects and one lipid class, the diacylglycerols, was increased in
coronary artery disease subjects.

[0015] The lipidomic approach uses one or more of three groups of lipid
analytes: [0016] (i) modified ceramides (modCER), modified
phosphatidylcholines (modPC) and, modified cholesterol esters (modCE)
selected from those listed in Table 1; [0017] (ii) two or more
non-modified lipid analytes selected from the list in Table 1; and/or
[0018] (iii) two or more lipid analytes wherein at least one is a
modified lipid analyte (modCER, modPC and/or modCE) and at least one is a
non-modified lipid analyte selected from the list in Table 1.

[0019] The levels or ratios of levels the lipidomic analytes are
determined relative to a control. The assay may also be automated or
semi-automated. In particular, the levels or ratios of, levels may be
used as input data for multivariate or univariate analysis leading to an
algorithm which can be used to generate an index of probability of having
or progressing with heart disease.

[0020] The levels of the lipid biomarkers may also be used in combination
with other standard indicators of heart disease, whether biochemical
markers, symptoms or electrocardial techniques.

[0021] Accordingly, one aspect of the present invention is directed to an
assay to stratify a subject as a vulnerable or non-vulnerable subject
with respect to plaques, the assay comprising determining the levels of a
lipid analyte selected from the list consisting of: [0022] (i) one or
more modified lipid analytes listed in Table 1; [0023] (ii) two or more
non-modified lipid analytes listed in Table 1, and [0024] (iii) two or
more lipid analytes wherein at least one is a modified lipid analyte
listed in Table 1 and at least one is a non-modified lipid analyte listed
in Table 1; wherein the level or ratio of the lipid analyte or analytes
relative to a control identifies the subject as being vulnerable or
non-vulnerable.

[0025] Yet another aspect of the present invention contemplates an assay
to stratify a subject with respect to heart disease, the assay comprising
determining the levels of a lipid analyte selected from the list
consisting of: [0026] (i) one or more modified lipid analytes listed in
Table 1; [0027] (ii) two or more non-modified lipid analytes listed in
Table 1, and/or [0028] (iii) two or more lipid analytes wherein at least
one is a modified lipid analyte listed in Table 1 and at least one is a
non-modified lipid analyte listed in Table 1; wherein the level or ratio
of the lipid analyte or analytes relative to a control provides a
correlation as to the presence, state, classification or progression of
heart disease.

[0029] In some embodiments, the assays comprise determining the levels of
at least two lipid analytes.

[0030] Still another aspect of the present invention contemplates the use
of a panel of lipid analytes selected from the list consisting of:
[0031] (i) one or more modified lipid analytes listed in Table 1; [0032]
(ii) two or more non-modified lipid analytes listed in Table 1, and
[0033] (iii) two or more lipid analytes wherein at least one is a
modified lipid analyte listed in Table 1 and at least one is a
non-modified lipid analyte listed in Table 1; in the manufacture of an
assay to identify the presence, state, classification or progression of
heart disease in a subject. In particular embodiments, the assay is used
to identify vulnerable or non-vulnerable subjects.

[0034] Even yet another aspect of the present invention relates to a
method of treatment or prophylaxis of a subject comprising assaying the
subject with respect to heart disease by determining the levels of a
lipid analyte selected from the list consisting of: [0035] (i) one or
more modified lipid analytes listed in Table 1; [0036] (ii) two or more
non-modified lipid analytes listed in Table 1, and [0037] (iii) two or
more lipid analytes wherein at least one is a modified lipid analyte
listed in Table 1 and at least one is a non-modified lipid analyte listed
in Table 1; wherein the level or ratio of the lipid analyte or analytes
relative to a control provides a correlation to the presence, state,
classification or progression of heart disease and then providing
therapeutic and/or behavioural modification to the subject.

[0038] The "stratification" is in effect a level of risk that a subject
has heart disease or is developing heart disease or is likely to develop
symptoms of heart disease.

[0039] The determination of the levels or ratios of the lipid biomarkers
may be used in combination with other indicators of heart disease and may
be used to monitor efficacy of treatment. In addition, the assay may be
useful in determining the most effective therapeutic or behavioural
intervention to treat heart disease in symptomatic or asymptomatic
subjects.

[0040] The assay may also be used in a personalized medicine approach in
the management of heart disease and/or as part of a pathology
architecture platform.

[0041] The above summary is not and should not be seen in any way as an
exhaustive recitation of all embodiments of the present invention.

[0043] Some figures contain color representations or entities. Color
photographs are available from the Patentee upon request or from an
appropriate Patent Office. A fee may be imposed if obtained from a Patent
Office.

[0044] FIGS. 1(A) and (B) are graphical representations of the area under
the curve and error rate resulting from stable CAD vs unstable CAD
models. Recursive feature elimination (RFE) with three-fold cross
validation (repeated 100 times) was used to develop multivariate models
using support vector machine learning. This was done for models of
varying feature size (e.g., 1, 2, 4, 8, 16, 32 and 64) and for models
that included either traditional risk factors alone (blue circles) lipids
alone (green squares) or lipids with traditional risk factors (red
triangles). ROC analysis was performed to give area under the curve
(panel A) and error rates (panel B). Error bars represent 95% confidence
limits.

[0045] FIGS. 2(A) and (B) are graphical representations of the area under
the curve and error rate resulting from control vs CAD models. Recursive
feature elimination (RFE) with three-fold cross validation (repeated 100
times) was used to develop multivariate models using support vector
machine learning. This was done for models of varying feature size (e.g.,
1, 2, 4, 8, 16, 32 and 64) and for models that included either
traditional risk factors alone (blue circles) lipids alone (green
squares) or lipids with traditional risk factors (red triangles). ROC
analysis was performed to give area under the curve (panel A) and error
rates (panel B). Error bars represent 95% confidence limits.

[0046]FIG. 3 is a graphical representation of ROC analysis of
classification models of stable CAD vs unstable CAD. Multivariate models
created with either the 13 traditional risk factors (Table 5), the 8
highest tanked lipids (Table 13) or a combination of both were validated
by three-fold cross validation repeated 10 times and the results combined
in a ROC analyses.

[0047]FIG. 4 is a graphical representation of ROC analysis of
classification models of control vs CAD. Multivariate models created with
either the 13 traditional risk factors (Table 5), the 16 highest ranked
lipids (Table 14) or a combination of both were validated by three-fold
cross validation repeated 10 times and the results combined in a ROC
analyses.

[0048]FIG. 5 provides a graphical representations of data showing
recursive feature elimination analysis of CAD. Multivariate models
containing different numbers of lipids alone (circles) or traditional
risk factors (squares) or combined lipids and risk factors (triangles)
were created to discriminate between control and CAD (left panels) and
between stable and unstable CAD (right panels). C-statistics (top panels)
and % accuracy (lower panels) with 95% confidence intervals for each
model are plotted against the number of variables in the model.

[0050] FIG. 7 provides graphical representations of data showing plasma
levels of selected lipid species. Lipid species were measured in each
group as described in Materials and Methods. The concentration of each
lipid species expressed as pmol/mL is plotted for each group. The bar
represents the median value, the box represents the 25th to
75th percentile and the whiskers the upper and lower limits. Circles
show outliers (>1.5× height of the box from the median) and
asterisks show extreme outliers (>3.0× height of the box from
the median).

BRIEF DESCRIPTION OF THE TABLES

[0051] Table 1 provides a numbered list of 331 lipid analytes (biomarkers)
identified in predetermined control vulnerable or non-vulnerable
subjects, normal (healthy) subjects or heart disease subjects. Numbers
prefaced by "s" identify internal standards used as internal controls for
lipid analysis as described in the Examples.

[0079] Throughout this specification and the claims which follow, unless
the context requires otherwise, the word "comprise", and variations such
as "comprises" and "comprising", will be understood to imply the
inclusion of a stated integer or step or group of integers or steps but
not the exclusion of any other integer or step or group of integers or
steps.

[0080] As used in the subject specification, the singular forms "a", "an"
and "the" include plural aspects unless the context clearly dictates
otherwise. Thus, for example, reference to "a biomarker" includes a
single biomarker, as well as two or more biomarkers; reference to "an
analyte" includes a single analyte or two or more analytes; reference to
"the invention" includes single and multiple aspects of the invention;
and so forth.

[0081] The use of numerical values in the various ranges specified in this
application, unless expressly indicated otherwise, are stated as
approximations as though the minimum and maximum values within the stated
ranges were both preceded by the word "about". In this manner, slight
variations above and below the stated ranges can be used to achieve
substantially the same results as values within the ranges. Also, the
disclosure of these ranges is intended as a continuous range including
every value between the minimum and maximum values. In addition, the
present invention extends to ratios of two or more markers providing a
numerical value associated with a level of risk of heart disease
development or presence.

[0082] A rapid, efficient and sensitive assay is provided for the
stratification of heart disease in symptomatic and asymptomatic subject.

[0083] "Stratification" includes identification, diagnosing,
clarification, monitoring and/or determination of the presence, level,
severity, state and/or classification of heart disease. Generally, this
is based on comparing a knowledge base of levels or ratios of lipid
analytes in body fluid or tissue extract to another knowledge base of
predetermined levels, statistically correlated to heart disease or a
condition or symptom within the spectrum of heart disease.

[0084] Hence, the present invention identifies a correlation between the
level or ratios of particular lipid analytes in a subject and heart
disease. The term "heart disease" as used herein is to be considered as
an individual condition as well as a spectrum of conditions including a
range of risk indicators of the level of disease progression. This risk
ranges from minor to extreme. The ability to monitor and identify markers
of heart disease enables decisions on the type of medical intervention
required from behavioural modification and medicaments to surgical
intervention. This is particularly the case with asymptomatic individuals
or those having a family history of heart disease.

[0085] The present invention extends to any or all conditions within the
clinical spectrum of "heart disease".

[0087] Reference herein to a "subject" includes a human which may also be
considered an individual, patient, host, recipient or target. The subject
may also be an animal or an animal model. The term "analyte" includes a
biomarker, marker, indicator, risk factor and the like.

[0088] The lipidomic approach uses one or more of three groups of lipid
analytes: [0089] (i) modified ceramides (modCER), modified
phosphatidylcholines (modPC) and modified cholesterol esters (modCE)
selected from those listed in Table 1; [0090] (ii) two or more
non-modified lipid analytes selected from the list in Table 1; and/or
[0091] (iii) two or more lipid analytes wherein at least one is a
modified lipid analyte (modCER, modPC and/or modCE) and at least one is a
non-modified lipid analyte selected from the list in Table 1.

[0092] Accordingly, one aspect of the present invention is directed to an
assay to stratify a subject as a vulnerable or non-vulnerable subject
with respect to plaques, the assay comprising determining the levels of a
lipid analyte selected from the list consisting of: [0093] (i) one or
more modified lipid analytes listed in Table 1; [0094] (ii) two or more
non-modified lipid analytes listed in Table 1, and [0095] (iii) two or
more lipid analytes wherein at least one is a modified lipid analyte
listed in Table 1 and at least one is a non-modified lipid analyte listed
in Table 1; wherein the level or ratio of the lipid analyte or analytes
relative to a control identifies the subject as being vulnerable or
non-vulnerable.

[0096] The present invention enables, therefore, a risk profile to be
determined for a subject based on a lipidomic profile. The stratification
or profiling enables early diagnosis, conformation of a clinical
diagnosis, treatment monitoring and treatment selection.

[0097] In a particular embodiment, the lipidomic profile is associated
with heart disease, the predisposition of development and/or the risk
level for severity and progression.

[0098] In, a particular embodiment, the invention provides an assay to
stratify a subject as a vulnerable or non-vulnerable subject with respect
to plaques, the assay comprising determining the levels of at least two
lipid analytes selected from the list consisting of: [0099] (i) one or
more modified lipid analytes listed in Table 1; [0100] (ii) two or more
non-modified lipid analytes listed in Table 1; and/or [0101] (iii) two or
more lipid analytes wherein at least one is a modified lipid analyte
listed in Table 1 and at least one is a non-modified lipid analyte listed
in Table 1; wherein the level of an individual lipid analyte listed in
Table 1 is different between vulnerable subjects and non-vulnerable
subjects and wherein the level of the lipid analytes in the subject
relative to a control identifies the subject as being vulnerable or
non-vulnerable.

[0102] In another embodiment, the assays comprise comparing the level of
the at least two lipid analytes in the subject to the respective levels
of the same lipid analytes in at least one control subject selected from
a vulnerable subject and a non-vulnerable subject, wherein a similarity
in the respective levels of the at least two lipid analytes between the
subject and the non-vulnerable subject identifies the subject as being
non-vulnerable, and wherein a similarity in the respective levels of the
at least two lipid analytes between the subject and the vulnerable
subject identifies the subject as being vulnerable.

[0103] Reference to a "control" broadly includes data that the skilled
person would use to facilitate the accurate interpretation of technical
data. In an illustrative example, the level or levels of lipid analyte(s)
from a subject are compared to the respective level or levels of the same
lipid analyte(s) in one or more cohorts (populations/groups) of control
subjects selected from a vulnerable subject cohort wherein the subjects
have been diagnosed with unstable heart disease, a non-vulnerable subject
cohort wherein the subjects have been diagnosed with stable heart
disease, a normal subject cohort wherein the subjects have been
predetermined not to have heart disease, and a heart disease subject
cohort that comprises the members of the vulnerable and non-vulnerable
cohorts. In some embodiments, the control may be the level or ratio of
one or more lipid analytes in a sample from the test subject taken at an
earlier time point. Thus, a temporal change in analyte levels can be used
to identify vulnerability or provide a correlation as to the state of
heart diseases. In some embodiments, the relative levels of two or more
lipid analytes provides a useful control.

[0104] In some embodiments, a control subject is a group of control
subjects. The level of analytes in a control subject group may be a mean
value or a preselected level, threshold or range of levels that define,
characterise or distinguish a particular group. Thresholds may be
selected that provide an acceptable ability to predict diagnostic or
prognostic risk, treatment success, etc. In illustrative examples,
receiver operating characteristic (ROC) curves are calculated by plotting
the value of one or more variables versus its relative frequency in two
populations (called arbitrarily "disease" and "normal" or "low risk" and
"high risk" groups for example). For any particular lipid analyte(s) or
class(es), a distribution of level(s) for subjects in the two populations
will likely overlap. Under such conditions, a test level does not
absolutely distinguish "disease" and "normal" or "vulnerable" and
"non-vulnerable" with 100% accuracy, and the area of overlap indicates
where the test cannot distinguish between groups. Accordingly, in some
embodiments, a threshold or range is selected, above which (or below
which, depending on how a lipid analyte level changes with heart disease
or prognosis) the test is considered to be "positive" and below which the
test is considered to be "negative". As described in Example 4,
non-parametric tests were used to establish the statistical significance
of differences between different analyte levels in the different control
groups (See Table 16). Linear regression analysis was also used to
identify lipid analytes that are independent predictors of group
assignment. Several lipid analytes were found to be independent predictor
of stable or unstable CAD, specifically PI 34:0, DHC 18:1, modCer
703.6.5.87, SM 22:1 and GM3 18:0. Similarly, twenty one lipid analytes
were able to distinguish individually between control and CAD patients
(Table 12, Model 6). Multivariate analysis is particularly suitable for
developing a predictive model based on plasma lipid profiles. A range of
models including different numbers of lipid analytes (1, 2, 4, 8, 16, 22,
64 . . . 329) either alone or with traditional risk factors were examined
for their ability to distinguish a particular group (Tables 18 to 20).
The values from these models were used to perform ROC analyses to
determine the severity and specificity of the models (see Example 6, FIG.
6). Accordingly it is possible, as demonstrated, herein to use the full
range of lipid analytes or to select particular subsets of lipid analytes
capable of distinguishing between particular groups.

[0105] Alternatively, or in addition, thresholds may be established by
obtaining an analyte level from the same patient, to which later results
may be compared. In these embodiments, the individual in effect acts as
their own "control group." In markers that increase with disease severity
or prognostic risk, an increase over time in the same patient can
indicate a worsening or development of disease or risk of disease or a
failure of a treatment regimen, while a decrease over time can indicate
remission of disease or success of a treatment regimen. Various further
controls will be routinely applied by the skilled artisan. In an
illustrative example, the levels of a range or panel of lipid analytes
within one or more lipid class are determined and compared to
predetermined levels in one or more control subject groups. Lipid
analytes determined herein not to be correlated with heart disease or
unstable plaques can be included as internal controls and are therefore
also useful in some embodiments.

[0106] In some embodiments, lipid analyte levels in control groups are
used to generate a profile of lipid analyte levels reflecting difference
between levels in two control groups. Thus, a particular lipid analyte
may be more abundant or less abundant in one control group compared to
another control group. The data may be represented as an overall
signature score or the profile may be represented as a barcode or other
graphical representation. The lipid analyte levels from a test subject
may be represented in the same way and similarity with the signature
scope or level of "fit" to a signature barcode or other graphical
representation may be determined. In other embodiments, the levels of a
particular lipid analyte or lipid class are analysed and a downward or an
upward trend in analyte level determined. Thus, for example, as shown in
the Examples, the total PI species were 13.8% lower in unstable vs stable
CAD, over and above a 13.5% decrease in the CAD group compared to control
groups. In another Example, lower levels of LPC species (except LPC 20:4
and LPC 20:2) were found to be predictive of disease severity/unstable
CAD, e.g. LPC 16:1 and LPC 14:0. In another example, SMI 018:0 was over
represented in the unstable CAD group.

[0107] In another embodiment, the assays further comprise comparing the
level of the at least two lipid analytes in the subject to the respective
levels of the same lipid analytes in at least one normal subject, wherein
a similarity in the respective levels of the at least two lipid analytes
between the subject and the normal subject identifies the subject as
being normal with respect to plaques.

[0109] In some embodiments, the lipid analytes are selected that fall
within a single lipid class. Thus, in some embodiments, the level of two
or more lipid analytes in one or more lipid classes are determined and
compared.

[0110] In some particular embodiments, the assays further comprise
determining the levels of at least two lipid analytes selected from the
list consisting of: [0111] (i) one or more modified lipid analytes
listed in Table 1; [0112] (ii) two or more non-modified lipid analytes
listed in Table 1; and/or [0113] (iii) two or more lipid analytes wherein
at least one is a modified lipid analytes listed in Table 1 and at least
one is a non-modified lipid analyte listed in Table 1; wherein the level
of an individual lipid analyte listed in Table 1 is different between
normal subjects and heart disease subjects and wherein the level of the
lipid analytes in the subject relative to a control identifies the
subject as being a normal subject or a heart disease subject.

[0114] In some embodiments, the or each modified lipid analyte in (i) is
selected from a modified ceramide (modCER) and a modified
phosphatidylcholine (modPC).

[0115] In other embodiments, the non-modified lipid analytes in (ii) are
selected from a dihexosylceramide (DHC), a sphingomyelin (SM), a
phosphatidylinositol (PI), a lysophosphatidylcholine (LPC), a
phosphatidylcholine (PC), an alkylphosphatidylcholine (APC), a
cholesterol ester (CE), a diacylglycerol (DG) and a triacylglycerol (TG).

[0116] In still further embodiments of the assay, the or each modified
lipid analyte in (iii) is selected from a modified ceramide (modCER) and
a modified phosphatidylcholine (modPC) and the or each non-modified lipid
in (iii) is selected from a dihexosylceramide (DHC), a sphingomyelin
(SM), a phosphatidylinositol (PI), a lysophosphatidylcholine (LPC), a
alkylphosphatidylcholine (APC), a cholesterol ester (CE), a
diacylglycerol (DG) and a triacylglycerol (TG).

[0119] In particular embodiments, the assayed levels of lipid analytes are
used in combination with one or more traditional risk factors selected
from age, sex, smoker, diabetes, hypertension, CAD family history, BMI,
total cholesterol, LDL, HDL, triglycerides, glucose and hsCRP to thereby
identify the subject as being vulnerable or non-vulnerable.

[0120] Suitably, the assays comprise, in some embodiments, comparing the
level of the at least two lipid analytes in the subject to the respective
levels of the same lipid analytes in at least one control subject
selected from a normal subject and a heart disease subject, wherein a
similarity in the respective levels of the at least two lipid analytes
between the subject and the heart disease subject identifies the subject
as having heart disease, and wherein a similarity in the respective
levels of the at least two lipid analytes between the subject and the
normal subject identifies the subject as being normal with respect to
heart disease.

[0128] In some further embodiments, the assayed levels of lipid analytes
are used in combination with one or more traditional risk factors
selected from age, sex, smoker, diabetes, hypertension, CAD family
history, BMI, total cholesterol, LDL, HDL, triglycerides, glucose and
hsCRP to thereby identify the subject as being normal or having heart
disease.

[0129] In a different embodiment, the present invention contemplates an
assay to stratify a subject with respect to heart disease, the assay
comprising determining the levels of a lipid analyte selected from the
list consisting of:

[0130] one or more modified lipid analytes listed in Table 1; [0131]
(ii) two or more non-modified lipid analytes listed in Table 1, and
[0132] (iii) two or more lipid analytes wherein at least one is a
modified lipid analyte listed in Table 1 and at least one is a
non-modified lipid analyte listed in Table 1; wherein the level or ratio
of the lipid analyte or analytes relative to a control provides a
indication or correlation as to the presence, absence state,
classification or progression of heart disease.

[0133] In particular embodiments, the invention provides an assay to
stratify a subject with respect to heart disease, the assay comprising
determining the levels of at least two lipid analytes selected from the
list consisting of: [0134] (i) one or more modified lipid analytes
listed in Table 1; [0135] (ii) two or more non-modified lipid analytes
listed in Table 1, and/or [0136] (iii) two or more lipid analytes wherein
at least one is a modified lipid analyte listed in Table 1 and at least
one is a non-modified lipid analyte listed in Table 1; wherein the level
of an individual lipid analyte listed in Table 1 is different between
normal and heart disease subjects and wherein the level of the lipid
analytes in the subject relative to a control provides an indication of
the presence or absence of heart disease.

[0137] In some embodiments, the assays comprise comparing the level of the
at least two lipid analytes in the subject to the respective levels of
the same lipid analytes in at least one control subject selected from a
normal subject and a heart disease subject, wherein a similarity in the
respective levels of the at least two lipid analytes between the subject
and the heart disease subject identifies the subject having heart
disease, and wherein a similarity in the respective levels of the at
least two lipid analytes between the subject and the normal subject
identifies the subject as a normal subject with respect to heart disease.

[0144] In some further embodiments, the assayed levels of lipid analytes
are used in combination with one or more traditional risk factors
selected from age, sex, smoker, diabetes, hypertension, CAD family
history, BMI, total cholesterol, LDL, HDL, triglycerides, glucose and
hsCRP to thereby identify the subject as being normal or having heart
disease.

[0145] Still another aspect of the present invention contemplates the use
of a panel of lipid analytes selected from the list consisting of:
[0146] (i) one or more modified lipid analytes listed in Table 1; [0147]
(ii) two or more non-modified lipid analytes listed in Table 1, and
[0148] (iii) two or more lipid analytes wherein at least one is a
modified lipid analyte listed in Table 1 and at least one is a
non-modified lipid analyte listed in Table 1; in the manufacture of an
assay to identify the presence, state, classification or progression of
heart disease in a subject.

[0150] The lipidomic profile further enables determination of endpoints in
pharmacotranslational studies. For example, clinical trials can take many
months or even years to establish the pharmacological parameters for a
medicament to be used in coronary care. However, these parameters may be
associated with a lipidomic profile associated with a health state.
Hence, the clinical trial can be expedited by first selecting a
medicament and pharmaceutical parameters which result in a lipidomic
profile associated with the desired health state.

[0151] Accordingly, another aspect of the present invention contemplates a
method for determining the pharmacoefficacy of a medicament for use in
heart disease treatment, the method comprising selecting a medicament and
its concentration and/or formulation parameters which provide a lipidomic
profile associated or characteristic of a healthy individual, the
lipidomic profile identified by determining the levels of a lipid analyte
selected from the list consisting of: [0152] (i) one or more modified
lipid analytes listed in Table 1; [0153] (ii) two or more non-modified
lipid analytes listed in Table 1, and [0154] (iii) two or more lipid
analytes wherein at least one is a modified lipid analyte listed in Table
1 and at least one is a non-modified lipid analyte listed in Table 1;
wherein the level or ratio of the lipid analyte or analytes relative to a
control provides a correlation as to the presence, state, classification
or progression of heart disease.

[0155] Another aspect of the present invention provides a method for
conducting a clinical trial for a medicament for the treatment or
prophylaxis of heart disease, the method comprising conducting the
clinical trial using a formulation of the medicament which generates a
lipidomic profile associated or characteristic of a healthy individual,
the lipidomic profile identified by determining the levels of a lipid
analyte selected from the list consisting of: [0156] (i) one or more
modified lipid analytes listed in Table 1; [0157] (ii) two or more
non-modified lipid analytes listed in Table 1, and [0158] (iii) two or
more lipid analytes wherein at least one is a modified lipid analyte
listed in Table 1 and at least one is a non-modified lipid analyte listed
in Table 1; wherein the level or ratio of the lipid analyte or analytes
relative to a control provides a correlation as to the presence, state,
classification or progression of heart disease.

[0159] The lipidomic profile, therefore, can be used as a marker to define
a desired state of health in an individual. It can be considered,
therefore, a defined surrogate endpoint or desired endpoint in clinical
management of subjects having heart disease treatment.

[0160] There are many methods which may be used to detect lipid analyte
levels including mass spectrometry. In a particular, liquid
chromatography, electrospray ionization-tandem mass spectrometry is used.

[0161] Immunological assays can also be done in any convenient formats
known in the art. These include Western blots, immunohistochemical assays
and ELISA assays. Any means for detecting a level of a lipid analyte can
be used in accordance with the present invention.

[0162] The biological sample is any fluid or cell or tissue extract in a
subject which comprises lipids. In one embodiment, the biological sample
is a tissue of the heart or surrounding the heart. In another embodiment,
the biological sample includes blood, plasma, serum, lymph, urine and
saliva or cell extracts.

[0163] The present invention identifies the presence of a lipidomic
profile associated with heart disease or a risk of developing same. In
order to detect a lipid analyte, a biological sample is prepared and
analyzed for a difference in levels or ratios of levels between the
subject being tested and a control. In this context, a "control" includes
the levels in a statistically significant normal population.

[0164] The identification of the association between the pathophysiology
of heart disease and levels of or ratios of lipids permits the early
presymptomatic screening of individuals to identify those at risk for
developing heart disease or to identify the cause of such a disorder or
the risk that any individual will develop same. The subject assay enables
practitioners to identify or stratify individuals at risk for certain
behavioural states associated with heart disease or its manifestations
including an inability to overcome symptoms of heart disease after
initial treatment. Certain behavioural or therapeutic or dietary
protocols may then be introduced to reduce the risk of developing heart
disease. Presymptomatic diagnosis will enable better treatment of heart
disease, including the use of existing medical therapies. Lipidotyping of
individuals is useful for (a) identifying a form of heart disease which
will respond to particular drugs, (b) identifying types of heart disease
which responds well to specific medications or medication types with
fewer adverse effects and (c) guide new drug discovery and testing.

[0165] Even yet another aspect of the present invention relates to a
method of treatment or prophylaxis of a subject comprising assaying the
subject with respect to heart disease by determining the levels of a
lipid analyte selected from the list consisting of: [0166] (i) one or
more modified lipid analytes listed in Table 1; [0167] (ii) two or more
non-modified lipid analytes listed in Table 1, and [0168] (iii) two or
more lipid analytes wherein at least one is a modified lipid analyte
listed in Table 1 and at least one is a non-modified lipid analyte listed
in Table 1; wherein the level or ratio of the lipid analyte or analytes
relative to a control provides a correlation to the presence, state,
classification or progression of heart disease and then providing
therapeutic and/or behavioural modification to the subject.

[0169] The present invention further provides a web-based system where
data on expression levels of lipids are provided by a client server to a
central processor which analyses and compares to a control and optionally
considers other information such as patient age, sex, weight and other
medical conditions and then provides a report, such as, for example, a
risk factor for disease severity or progression or status or an index of
probability of heart disease in symptomatic or asymptomatic individuals.

[0170] Hence, knowledge-based computer software and hardware also form
part of the present invention.

[0171] In particular, the assays of the present invention may be used in
existing or newly developed knowledge-based architecture or platforms
associated with pathology services. For example, results from the assays
are transmitted via a communications network (e.g. the internet) to a
processing system in which an algorithm is stored and used to generate a
predicted posterior probability value which translates to the index of
disease probability which is then forwarded to an end user in the form of
a diagnostic or predictive report.

[0172] The assay may, therefore, be in the form of a kit or computer-based
system which comprises the reagents necessary to detect the concentration
of the lipid biomarkers and the computer hardware and/or software to
facilitate determination and transmission of reports to a clinician.

[0173] The assay of the present invention permits integration into
existing or newly developed pathology architecture or platform systems.
For example, the present invention contemplates a method of allowing a
user to determine the status of a subject with respect to a heart disease
or subtype thereof or stage of heart disease, the method including:
[0174] (a) receiving data in the form of levels or concentrations of a
lipid analyte selected from the list consisting of: [0175] (i) one or
more modified lipid analytes listed in Table 1; [0176] (ii) two or more
non-modified lipid analytes listed in Table 1, and [0177] (iii) two or
more lipid analytes wherein at least one is a modified lipid analyte
listed in Table 1 and at least one is a non-modified lipid analyte listed
in Table 1; [0178] wherein the level or ratio of the lipid analyte or
analytes relative to a control provides a correlation to the presence,
state, classification or progression of heart disease; [0179] from the
user via a communications network; [0180] (b) processing the subject
data via multivariate analysis to provide a disease index value; [0181]
(c) determining the status of the subject in accordance with the results
of the disease index value in comparison with predetermined values; and
[0182] (d) transferring an indication of the status of the subject to the
user via the communications network reference to the multivariate
analysis includes an algorithm which performs the multivariate or
univariate analysis function.

[0183] Conveniently, the method generally further includes: [0184] (a)
having the user determine the data using a remote end station; and [0185]
(b) transferring the data from the end station to the base station via
the communications network.

[0186] The base station can include first and second processing systems,
in which case the method can include: [0187] (a) transferring the data
to the first processing system; [0188] (b) transferring the data to the
second processing system; and [0189] (c) causing the first processing
system to perform the multivariate analysis function to generate the
disease index value.

[0190] The method may also include: [0191] (a) transferring the results
of the multivariate analysis function to the first processing system; and
[0192] (b) causing the first processing system to determine the status of
the subject.

[0193] In this case, the method also includes at lest one of: [0194] (a)
transferring the data between the communications network and the first
processing system through a first firewall; and [0195] (b) transferring
the data between the first and the second processing systems through a
second firewall.

[0196] The second processing system may be coupled to a database adapted
to store predetermined data and/or the multivariate analysis function,
the method include: [0197] (a) querying the database to obtain at least
selected predetermined data or access to the multivariate analysis
function from the database; and [0198] (b) comparing the selected
predetermined data to the subject data or generating a predicted
probability index.

[0199] The second processing system can be coupled to a database, the
method including storing the data in the database.

[0200] The method can also include having the user determine the data
using a secure array, the secure array of elements capable of determining
the level of biomarker and having a number of features each located at
respective position(s) on the respective code.

[0201] In this case, the method typically includes causing the base
station to: [0202] (a) determine the code from the data; [0203] (b)
determine a layout indicating the position of each feature on the array;
and [0204] (c) determine the parameter values in accordance with the
determined layout, and the data.

[0205] The method can also include causing the base station to: [0206]
(a) determine payment information, the payment information representing
the provision of payment by the user; and [0207] (b) perform the
comparison in response to the determination of the payment information.

[0208] The present invention also provides a base station for determining
the status of a subject with respect to a heart disease or a subtype
thereof or a stage of heart disease, the base station including: [0209]
(a) a store method; [0210] (b) a processing system, the processing system
being adapted to: [0211] (c) receive subject data from the user via a
communications network, the data including levels or concentrations of a
lipid analyte selected from the list consisting of: [0212] (i) one or
more modified lipid analytes listed in Table 1; [0213] (ii) two or more
non-modified lipid analytes listed in Table 1, and [0214] (iii) two or
more lipid analytes wherein at least one is a modified lipid analyte
listed in Table 1 and at least one is a non-modified lipid analyte listed
in Table 1; [0215] wherein the level or ratio of the lipid analyte or
analytes relative to a control provides a correlation to the presence,
state, classification or progression of heart disease; [0216] (d)
performing an algorithmic function including comparing the data to
predetermined data; [0217] (e) determining the status of the subject in
accordance with the results of the algorithmic function including the
comparison; and [0218] (f) output an indication of the status of the
subject to the user via the communications network.

[0219] The processing system can be adapted to receive data from a remote
end station adapted to determine the data.

[0220] The processing system may include: [0221] (a) a first processing
system adapted to: [0222] (i) receive the data; and [0223] (ii)
determine the status of the subject in accordance with the results of the
multivariate analysis function including comparing the data; and [0224]
(b) a second processing system adapted to: [0225] (i) receive the data
from the processing system; [0226] (ii) perform the multivariate or
univariate analysis function including the comparison; and [0227] (iii)
transfer the results to the first processing system.

[0228] The base station typically includes: [0229] (a) a first firewall
for coupling the first processing system to the communications network;
and [0230] (b) a second firewall for coupling the first and the second
processing systems.

[0231] The processing system can be coupled to a database, the processing
system being adapted to store the data in the database.

[0232] Still another aspect of the present invention contemplates the use
of a panel of lipid analytes selected from the list consisting of:
[0233] (i) one or more modified lipid analytes listed in Table 1; [0234]
(ii) two or more non-modified lipid analytes listed in Table 1, and
[0235] (iii) two or more lipid analytes wherein at least one is a
modified lipid analyte listed in Table 1 and at least one is a
non-modified lipid analyte listed in Table 1; in the manufacture of an
assay to identify the presence, state, classification or progression of
heart disease in a subject.

[0236] In another embodiment, the present invention contemplates an assay
for determining the presence of heart disease in a subject, the assay
comprising determining the concentration of a lipid analyte selected from
the list consisting of: [0237] (i) one or more modified lipid analytes
listed in Table 1; [0238] (ii) two or more non-modified lipid analytes
listed in Table 1, and [0239] (iii) two or more lipid analytes wherein at
least one is a modified lipid analyte listed in Table 1 and at least one
is a non-modified lipid analyte listed in Table 1; wherein the level or
ratio of the lipid analyte or analytes relative to a control provides a
correlation to the presence, state, classification or progression of
heart disease in a biological sample from the subject wherein an altered
concentration in the lipid or lipids is indicative of the subject having
heart disease.

[0240] In accordance with this embodiment, levels of the lipid(s) may be
screened alone or in combination with other biomarkers or heart disease
indicators. An "altered" level means an increase or elevation or a
decrease or reduction in the concentrations of the lipids.

[0241] The determination of the concentrations or levels of the biomarkers
enables establishment of a diagnostic rule based on the concentrations
relative to controls. Alternatively, the diagnostic rule is based on the
application of a statistical and machine learning algorithm. Such an
algorithm uses relationships between biomarkers and disease status
observed in training data (with known disease status) to infer
relationships which are then used to predict the status of patients with
unknown status. An algorithm is, employed which provides an index of
probability that a patient has heart disease or a state or form or class
thereof. The algorithm performs a multivariate analysis function.

[0242] Hence, the present invention provides a diagnostic rule based on
the application of statistical and machine learning algorithms. Such an
algorithm uses the relationships between lipidomic biomarkers and disease
status observed in training data (with known disease status) to infer
relationships which are then used to predict the status of patients with
unknown status. Practitioners skilled in the art of data analysis
recognize that many different forms of inferring relationships in the
training data may be used without materially changing the present
invention.

[0243] Hence, the present invention contemplates the use of a knowledge
base of training data comprising levels of lipid biomarkers from a
subject with a heart condition to generate an algorithm which, upon input
of a second knowledge base of data comprising levels of the same
biomarkers from a patient with an unknown heart disease condition,
provides an index of probability that predicts the nature of the heart
disease condition.

[0244] The term "training data" includes knowledge of levels of lipid
biomarkers relative to a control. A "control" includes a comparison to
levels of biomarkers in a subject devoid of the heart disease condition
or cured of the condition or may be a statistically determined level
based on trials. The term "levels" also encompasses ratios of levels of
lipid biomarkers.

[0245] Hence, the "training data" includes levels or ratios of one or more
of three groups of lipid analytes selected from [0246] (i) modified
ceramides (modCER), modified phosphatidylcholines (modPC) and modified
cholesterol esters (modCE) selected from those listed in Table 1; [0247]
(ii) two or more non-modified lipid analytes selected from the list in
Table 1; and/or [0248] (iii) two or more lipid analytes wherein at least
one is a modified lipid analyte listed in Table 1 (modCER, modPC and/or
modCE) and at least one is a non-modified lipid analyte, selected from
the list in Table 1.

[0249] The present invention further provides a panel of lipidomic
biomarkers useful in the detection of a heart disease, the panel
comprising lipid analytes selected from the list consisting of: [0250]
(i) one or more modified lipid analytes listed in Table 1; [0251] (ii)
two or more non-modified lipid analytes listed in Table 1, and [0252]
(iii) two or more lipid analytes wherein at least one is a modified lipid
analyte listed in Table 1 and at least one is a non-modified lipid
analyte listed in Table 1; wherein the level or ratio of the lipid
analyte or analytes, relative to a control provides a correlation to the
presence, state, classification or progression of heart disease.

[0254] Data generated from the levels of a lipid analyte selected from the
list consisting of: [0255] (i) one or more modified lipid analytes
listed in Table 1; [0256] (ii) two or more non-modified lipid analytes
listed in Table 1, and [0257] (iii) two or more lipid analytes wherein at
least one is a modified lipid analyte listed in Table 1 and at least one
is a non-modified lipid analyte listed in Table 1; are input data. The
input of data comprising the lipid analytes is compared with a control or
is put into the algorithm which provides a risk value of the likelihood
that the subject has, for example, heart disease. A treatment regime can
also be monitored as well as a likelihood of a relapse.

[0258] In context of the present disclosure, "fluid" includes any blood
fraction, for example serum or plasma, that can be analyzed according to
the methods described herein. By measuring blood levels of a particular
lipid biomarker(s), it is meant that any appropriate blood fraction can
be tested to determine blood levels and that data can be reported as a
value present in that fraction. Other fluids contemplated herein include
ascites, tissue exudate, urine, lymph fluid, mucus and respiratory fluid.

[0259] As described above, methods for diagnosing heart disease by
determining levels of specific identified lipid biomarkers as listed in
Table 1 and using these levels as second knowledge base data in an
algorithm generated with first knowledge base data or levels of the same
biomarkers in patents with a known disease. Also provided are methods of
detecting symptomatic heart disease comprising determining the presence
and/or velocity of specific identified lipid biomarkers in a subject's
sample. By "velocity" it is meant the change in the concentration of the
biomarker in a patient's sample over time.

[0260] The term "sample" as used herein means any sample containing lipid
analytes that one wishes to detect including, but not limited to,
biological fluids (including blood, plasma, serum, ascites), tissue
extracts, freshly harvested cells, and lysates of cells which have been
incubated in cell cultures. In a particular embodiment, the sample is
heart tissue, one or more plaque, blood, serum, plasma or ascites.

[0261] As indicated above, the "subject" can be any mammal, generally
human, suspected of having or having heart disease. The subject may be
symptomatic or asymptomatic.

[0262] The term "control sample" includes any sample that can be used to
establish a first knowledge base of data from subjects with a known
disease status.

[0263] The method of the subject invention may be used in the diagnosis
and staging of heart disease. The present invention may also be used to
monitor the progression of a condition and to monitor whether a
particular treatment is effective or not. In particular, the method can
be used to confirm the absence or amelioration of the symptoms of the
condition such as following surgery, stents, medication or behavioural
change.

[0264] In an embodiment, the subject invention contemplates a method for
monitoring the progression of heart disease in a patient, comprising:
[0265] (a) providing a sample from a patient; [0266] (b) determining the
level of a lipid analyte selected from the list consisting of: [0267]
(i) one or more modified lipid analytes listed in Table 1; [0268] (ii)
two or more non-modified lipid analytes listed in Table 1, and [0269]
(iii) two or more lipid analytes wherein at least one is a modified lipid
analyte listed in Table 1 and at least one is a non-modified lipid
analyte listed in Table 1; wherein the level or ratio of the lipid
analyte or analytes relative to a control provides a correlation to the
presence, state, classification or progression of heart disease
subjecting the levels to an algorithm to provide an index of probability
of the patient having heart disease; and [0270] (c) repeating steps (a)
and (b) at a later point in time and comparing the result of step (b)
with the result of step (c) wherein a difference in the index of
probability is indicative of the progression of the condition in the
patient.

[0271] In particular, an increased index of probability of a disease
condition at the later time point may indicate that the condition is
progressing and that the treatment (if applicable) is not being
effective. In contrast, a decreased index of probability at the later
time point may indicate that the condition is regressing and that the
treatment (if applicable) is effective.

[0272] The present invention further provides an algorithm-based screening
assay to screen samples from patients. Generally, input data are
collected based on levels of one or more lipid biomarkers and subjected
to an algorithm to assess the statistical significance of any elevation
or reduction in levels which information is then output data. Computer
software and hardware for assessing input data are encompassed by the
present invention.

[0273] Another aspect of the present invention contemplates a method of
treating a patient with heart disease the method comprising subjecting
the patient to a diagnostic assay to determine an index of probability of
the patient having the heart condition, the assay comprising determining
the levels of a lipid analyte selected from the list consisting of:
[0274] (i) one or more modified lipid analytes listed in Table 1; [0275]
(ii) two or more non-modified lipid analytes listed in Table 1, and
[0276] (iii) two or more lipid analytes wherein at least one is a
modified lipid analyte listed in Table 1 and at least one is a
non-modified lipid analyte listed in Table 1; wherein the level or ratio
of the lipid analyte or analytes relative to a control provides a
correlation to the presence, state, classification or progression of
heart disease and where there is a risk of the patient having the
condition, subjecting the patient to surgical intervention, medication
and/or behavioural change and then monitoring index of probability over
time.

[0277] Reference to an "algorithm" or "algorithmic functions" as outlined
above includes the performance of a multivariate or univariate analysis
function. A range of different architectures and platforms may be
implemented in addition to those described above. It will be appreciated
that any form of architecture suitable for implementing the present
invention may be used. However, one beneficial technique is the use of
distributed architectures. In particular, a number of end stations may be
provided at respective geographical locations. This can increase the
efficiency of the system by reducing data bandwidth costs and
requirements, as well as ensuring that if one base station becomes
congested or a fault occurs, other end stations could take over. This
also allows load sharing or the like, to ensure access to the system is
available at all times.

[0278] In this case, it would be necessary to ensure that the base station
contains the same information and signature such that different end
stations can be used.

[0279] It will also be appreciated that in one example, the end stations
can be hand-held devices, such as PDAs, mobile phones, or the like, which
are capable of transferring the subject data to the base station via a
communications network such as the Internet, and receiving the reports.

[0280] In the above aspects, the term "data" means the levels or
concentrations of the biomarkers. The "communications network" includes
the internet. When a server is used, it is generally a client server or
more particularly a simple object application protocol (SOAP).

[0281] A report outlining the likelihood of heart disease by the subject
is issued.

[0282] The present invention is further described by the following
non-limiting Examples. Materials and Methods used in these Examples are
provided below.

Materials and Methods

Sample Collection

[0283] Plasma samples from the CAD patients used in this study were
collected as part of a previous study conducted by White et al.
Cardiovascular Research 75:813-20, 2007. A total of 202 patients with de
novo presentation of CAD who were undergoing coronary angiography were
recruited (White et al. supra 2007). Patients who had undergone previous
coronary revascularization were excluded. Of the original 202 patients,
plasma samples from 143 were available for use in this project. Patients
were classified as either stable (n=61) or unstable (n=81) by two
independent cardiologists on the basis of their symptoms, 12-lead ECG and
cardiac troponin I measurements in accordance with the Braunwald criteria
(White et al. supra 2007; Braunwald E. Circulation 80:410-4, 1989).
Venous blood samples were collected into EDTA tubes. The plasma was
prepared by centrifugation (1000×g, 15 minutes at 4° C.) and
stored at -80° C. until required. Biochemical, lipid, and
hematological parameters as well as clinical characteristics were
measured. These included total cholesterol, LDL, high density lipoprotein
(HDL), blood pressure, C reactive protein (CRP), smoking status,
medications and body mass index (BMI).

[0284] Plasma samples from a cohort of 61 healthy individuals were
obtained and used as control samples. Patients were not receiving
medication for coronary vascular disease (CVD), diabetes or hypertension
and had no history of myocardial infarction (MI). Additionally, patients
displayed blood pressure <131/86 mm Hg, fasting total cholesterol
<5.6 mmol/L, fasting triglycerides <2.0 mmol/L and fasting plasma
glucose <6.1 mmol/L. Plasma was prepared by centrifugation
(1500×g, 10 minutes at 4° C.) within 24 hours of collection.
The plasma samples had not been thawed prior to this study.

Sample Preparation and Lipid Extraction

[0285] Plasma samples (200 μL) were thawed and treated with the
antioxidant butylhydroxytoluene (BHT) (1 μL of 100 mM in ethanol) and
immediately vortexed. Lipid extraction was performed using a modification
of the method of Folch et al. J Biol Chem 226:497-509, 1957. A 10 μL
aliquot of plasma was transferred to an eppendorf tube with 104 of
internal standard mix 1 and 5 μL of internal standard mix 2 (Table 2).
CHCl3/MeOH (2:1) (200 μL) was added followed by brief vortexing.
Samples were placed on a rotary mixer for ten minutes and then sonicated
in a water bath at room temperature for thirty minutes. After sonication,
the samples were incubated for twenty minutes at room temperature
followed by centrifugation (16,000×g, 10 minutes at room
temperature). The supernatant was transferred into a 0.5 mL polypropylene
96 well plate and dried under a stream of nitrogen at 40° C. The
samples were resuspended in 50 μL water saturated butanol followed by
ten minutes sonication. Then 50 μL of 10 mM ammonium formate in
methanol was added. The samples were centrifuged (3,350×g, 5
minutes at room temperature) and the supernatant transferred into 0.2 mL
micro-inserts placed into 32×11.6 mm glass vials with Teflon insert
caps. Once extracted the samples were immediately subjected to mass
spectrometry.

Mass Spectrometry

[0286] Lipid analysis was performed by liquid chromatography, electrospray
ionisation-tandem mass spectrometry (LC ESI-MS/MS) using a HP 1200 liquid
chromatography system combined with a PE Sciex API 4000 Q/TRAP mass
spectrometer with a turbo-ionspray source (350° C.) and Analyst
1.5 data system. A Zorbax C18, 1.8 μm, 50×2.1 mm column was used
for LC separation. The mobile phase consisted of
tetrahydrofuran:methanol:water in the ratios 30:20:50 (Solvent A) and
75:20:5 (Solvent B), both containing 10 mM NH4COOH. The following
gradient conditions were employed for all lipids except the DG and TG;
100% A/0% B reducing to 0% A/100% B over eight minutes followed by 2
minutes at 0% A/100% B, a return to 100% A/0% B over 0.5 minute then held
for 3.5 minutes at 100% A/0% B prior to the next injection. DG and TG
were separated using the same system with an isocratic flow at 15% A/85%
for 6 minutes between injections.

[0287] The optimisation of voltages for collision energy (CE),
declustering potential (DP), entrance potential (EP) and cell exit
potential (CXP) was carried out using the tuning and optimisation feature
of the instrument software (Analyst 1.5).

Nomenclature

[0288] The nomenclature (both systematic and common names) used in this
document has come primarily from the two recent publications on this
topic from the Lipid Maps Consortium (See Fahy et al., J Lipid Res.
51(6): 1618, 2010 and Fahy et al., J Lipid Res. 50: S9-14, 2009).

[0289] In addition, a number of terms have been used to define lipid
species where the full structure is not known but where characteristic
collision induced fragmentation data has provided us with a partial
structure of the lipid species. These are as follows

modPC xxx.x/yy.y=modified or undefined phosphocholine containing lipid
species with mass/charge ratio of the M+H ion denoted by xxx.x and
retention time under the presently disclosed defined chromatographic
conditions defined as yy.y minutes. modCer xxx.x/yy.y=modified or
undefined sphingosine containing lipid species with mass/charge ratio of
the M+H ion denoted by xxx.x and retention time under the presently
disclosed defined chromatographic conditions defined as yy.y minutes.
modCE xxx.x/yy.y=modified or undefined cholesterol containing lipid
species with mass/charge ratio of the M+H ion denoted by xxx.x and
retention time under the presently disclosed defined chromatographic
conditions defined as yy.y minutes.

[0292] 1-O-acylceramides, oxidized phosphatidylcholine (OxPC) and oxidized
cholesterol esters (OxCE) were thought to be potential biomarkers of the
presence and progression of CAD. To identify lipid species in each of
these classes, precursor ion scans were performed on a subset of 30
individuals (10 healthy controls, 10 stable CAD and 10 unstable CAD)
chosen at random from our cohort.

[0293] Identification of Modified Ceramides:

[0294] Precursor scans were performed to identify 1-O-acylceramide species
in plasma. Fragmentation of ceramides by CID in Q2 cleaves the bond
between the carbon and the nitrogen at the sphingoid base and, with the
loss of water, produces a daughter ion with a m/z 264.3 (Murphy et al.
Chem Rev 101:479-526, 2001). Thus a precursor ion scan for m/z 264 will
identify all modified ceramides including 1-O-acylceramides. These are
referred to collectively as modified ceramides (modCer). Two precursor
ion scans for m/z 264.3 were performed to cover the m/z ranges 530-760
for low molecular weight modCer and m/z 750-980 for high molecular weight
modCer (Table 3).

[0295] Identification of Modified Phosphatidylcholines:

[0296] OxPC species may include non-truncated OxPCs which involve the
addition of oxygen at the double bonds of the polyunsaturated acyl
moities (Davis et al. J Biol Chem 283:6428-37, 2008) or truncated oxPCs
where the oxidized acyl chains are cleaved to produce lower molecular
weight species. A precursor ion scan for m/z 184 will identify all
species of lipids containing a phosphocholine head group including
oxidized phosphatidylcholines. However other phosphocholine species may
also be identified, we have referred to these species as modified PC
(modPC). To cover the possible m/z ranges that would cover all OxPCs,
three precursor ion scan experiments were performed. The m/z ranges for
Q1 for these three experiments were 490-670, 640-820 and 800-980.
Fragmentation of phospholipids by CID of PC species produces a daughter
ion of 184.1 which was used as the m/z setting in Q3 (Davis et al. supra
2008, Cui and Thomas Journal of Chromatography B; 877:2709-15, 2009)
(Table 3).

[0297] Identification of Oxidized Cholesterol Esters:

[0298] As with phosphatidylcholine species, cholesterol esters which
contain polyunsaturated fatty acids are susceptible to oxidation. A
precursor ion scan of m/z 369 will identify all species of cholesterol
ester, those with oxidized fatty acids. These are referred to as modified
cholesterol esters (modCE). The mass ranges for the two precursor ion
scan experiments aimed at identifying modCEs were m/z 450-650 and m/z
650-850 in Q1 with a m/z setting of 369.3 for Q3 (Table 3).

[0299] Plasma Lipid Profiling:

[0300] MRM experiments were established for each of the new lipid
biomarkers identified from the precursor ion scans. These were then
combined with a larger set of MRM experiments that had been developed by
identifying the major species of each lipid class in plasma extracts
using precursor ion and neutral loss scans (Table 4 and as updated in
Table 15).

[0301] Plasma lipid profiling using these MRM experiments was performed on
each of the 202 plasma samples in the cohort in addition to 14 quality
control (QC) plasma samples. Each ion pair was monitored for between 10
and 50 ms (using scheduled MRM mode) with a resolution of 0.7 amu at
half-peak height and the area under the resulting chromatogram was
calculated. The peak area data was analysed using Applied Biosystems
Analyst 1.5. Raw data for each class was normalised against the internal
standard and converted into pmol per mL of plasma.

[0303] Data resulting from the precursor ion scans were analysed using
Markerview (version 1.2). Data were normalized against the respective
internal standard of the lipid class under investigation.

[0304] A Student's t-test was performed to identify which lipid analytes
were significantly different between stable and unstable CAD groups and
between control and CAD groups (stable and unstable CAD combined).
Analytes with a p value <0.1 that did not correspond to known lipid
species were then incorporated into the plasma profiling methods, these
lipid species were termed modCer, modPC and modCE.

[0305] Data Processing and Statistical Analysis of MRM Data:

[0306] Non-parametric, Mann-Whitney-U tests were used to determine the
analytes that were significantly different between stable vs unstable CAD
groups and the control vs CAD groups. Analysis of variance (ANOVA) was
performed on linear regression models to determine the relative
contribution of the traditional risk factors and lipid analytes to
classification models (SPSS version 17.0, SPSS Inc).

[0307] Multivariate analysis was applied for the creation of prediction
models. This analysis followed a statistical machine learning approach
and methodology comprising multiple cross-validation iterations to assess
the power of proposed solutions (National ICT Australia). Briefly,
recursive feature elimination (RFE) analysis with three-fold
cross-validation repeated multiple times (100) was applied to develop
multivariate models using support vector machine learning. This was done
for models of varying feature size (e.g., 2, 4, 8, 16, 32 and 64). The
output of this exercise was a ranked list of the lipids according to the
frequency of their recurrent incorporation in generated models. This
approach also allowed the removal of those highly correlated variables
that did not add significantly to the model. For each set of models with
different numbers of analytes Receiver Operator Characteristic (ROC)
analysis was performed, calculating Area Under the Curve (AROC).

[0308] ROC analysis is used extensively in diagnostic testing to determine
the performance of a given model (Fawcett T Pattern Recogn Lett
27:861-74, 2006).

Example 1

Patient Characteristics

[0309] The patients in the stable and unstable cohorts were closely
matched, with the exception of smoking status and hsCRP (Table 5). In
contrast, most of the clinical and biochemical parameters differed
significantly between the control cohort and the CAD cohort (combined
stable and unstable CAD patients) (Table 5).

[0310] The medication profile of the stable and unstable CAD patients was
examined for lipid lowering, antihypertensive, antiplatelet,
anticoagulant, anti-anginal anti-arrhythmic and anti-diabetic treatments.
X2 revealed that four medications were significantly different
between these two cohorts (Table 6). The medications that showed
differences were statins for the lipid lowering medications, angiotensin
II blockers and intravenous glycerol nitrate from the anti-hypertensive
medications and heparin infusion from the anticoagulant medications.

[0312] This software aligns and then tabulates the m/z and retention time
for all the peaks (also called features) within the precursor ions scans.
It then normalizes the data against the relevant internal standard. A
student t-test was then applied to the features to identify which were
different between stable and unstable CAD cohorts and between the control
and CAD cohorts, at a significance of p<0.10. The spectra of these
peaks were then examined to remove known lipid species and isotopes.

[0313] From this process a total of 75 markers (14 modCer, 57 modPC and 4
modCE) were selected across the three lipid classes, these markers are
shown in Table 7.

[0314] Each of the 202 plasma samples in the cohort was analyzed for a
total of 331 lipid species by the two scheduled MRM experiments (Tables 7
and 8). From the lipid concentrations in the 14 QC samples the
coefficients of variation (% CV) were determined across the entire
analytical run. % CV values were less than 20% for 271 of the 331 lipid
species. Those lipids which had a % CV greater that 20% were primarily
lipid species that were in low abundance (<200 pmol/mL) these did not
include the top ranking lipid analytes.

Example 4

Univariate Analysis

[0315] A Mann Whitney-U test was used to distinguish which lipids showed
significant differences between cohorts (stable CAD vs unstable CAD and
control vs CAD). This identified 73 lipids that were significantly
different between the stable and unstable CAD cohorts (p<0.05) and 198
lipids that showed statistical significance (p<0.05) between the
control and CAD cohorts and (Table 9). A summary of the total number of
lipids per lipid class that show differences between these cohorts is
shown in Table 10.

Anova

[0316] In order to identify lipids that were independent predictors of
class assignment linear regression analysis was performed. A number of
different models were created to analyse different subsets of the cohort
for covariates.

[0317] Models 1 to 3 were created with the stable CAD and unstable CAD
cohorts. Model one used only the 13 traditional risk factors (age, sex,
smoking status, diabetes, hypertension, family history of CAD, BMI, total
cholesterol, LDL, HDL, triglycerides, glucose and hsCRP, Table 5). Model
2 was created using only the lipids (see Table 9 and 10) and Model 3
included both the lipids as well as the traditional risk factors. The
ANOVA results and covariates that were independent predictors and showed
significance (p<0.05) are shown in Table 11. The partial correlation
values show the relative contribution of the independent variables to the
model when the linear effects of the other independent variables in the
models have been removed. From the R2 values (measure of the fit of the
model) it can be seen that model 3 (R2=0.473) shows the best fit
indicating that the combination of the lipid biomarkers and the
traditional risk factors provides a better classification of the stable
and unstable CAD cohorts than the traditional risk factors or the lipids
alone. Whilst CRP is the most significant sources of variation between
these two cohorts, the lipids PI 34:0. DHC 18:1, modCer 703.6/5.87, SM
22:1 and GM3 18:0 were also shown to be independent predictors.

[0318] Models 4, 5 & 6 represent models created with the control and CAD
cohorts using traditional risk factors alone, lipids alone or a
combination of both respectively. The fit of these models (R2 values
shown in Table 12) parallel that of the stable versus unstable CAD models
with the data showing an improvement in the fit to the predictive model
when traditional risk factors and lipids were combined. Twenty-one lipids
were identified as being able to distinguish between control and CAD
patients independently of all other factors (Table 12, model 6).

Example 5

Multivariate Analysis

[0319] Linear regression modeling was able to create models that examined
the influence of traditional risk factors, lipids and a combination of
these in classifying between stable and unstable CAD patients, and
control and CAD patients. However, given the complexity of the data set
and the large number of variables, multivariate modeling is more
appropriate to create a predictive model based upon the plasma lipid
profile (Bylesjo et al. Journal of Chemometrics 20:341-51, 2006).

[0320] Recursive feature elimination (RFE) analysis was applied using
three-fold cross validation (repeated 100 times) to develop multivariate
models using support vector machine learning. This was done for models of
varying feature size (e.g., 1, 2, 4, 8, 16, 32 and 64) and for models
that included either lipids alone or lipids with traditional risk
factors. The output of this exercise was a ranked list of the lipids
according to the frequency of their recurrent incorporation in the
generated models to distinguish stable CAD from unstable CAD (Table 13)
or control from CAD (Table 14). This approach also allowed the removal of
those significant but highly correlated variables that did not add
significantly to the model.

[0321] The Y predictor values from these models were used to perform
Receiver Operator Characteristic (ROC) analysis, which measures the
sensitivity and specificity of the model and can be used as a measure of
the model's ability to correctly classify cases (Stenlund et al.
Analytical Chemistry 80:6898-906, 2008). The area under the curve (AUC)
from these ROC analyses was potted against the number of variables to
identify the minimum number required for optimal discrimination (FIGS.
1(A) and (B) and 2(A) and (B)). In the models created to discriminate
between stable CAD and unstable CAD increasing the number of lipids in
the model increased the AUC which reached a maximum at 8-16 lipid
analytes (FIG. 1 panel A). Using a combination of traditional risk
factors and lipids gave the best discrimination with a maximum AUC
achieved with 8 features. FIG. 2, panel B shows that lipid only models
had a lower error rate that the traditional risk factor only models but
that the combined traditional risk factor and lipid models had the lowest
error rates.

[0322] Models created to distinguish control and CAD had higher AUC and
continued to show a slight increase up to 256 lipids although 16 lipids
was sufficient to produce an AUC of 0.94 (FIG. 2 panel A). Similar to the
stable CAD vs unstable CAD models, the combination of traditional risk
factors and lipids resulted in the highest AUC with 16 features showing
an AUC of 0.96. The combination of traditional risk factors and lipids
also resulted in the lowest error rates in the control vs CAD models
(FIG. 2, panel B).

[0323] The two models created with the 8 and 16 lipids (stable CAD vs
unstable CAD and control vs CAD) were compared to the models created with
the traditional risk factors and then to models created with a
combination of the traditional risk factors and the lipids. These
traditional risk factors included age, sex, smoking status, diabetes,
hypertension, family history of CAD, BMI, total cholesterol, LDL, HDL,
triglycerides, glucose and hsCRP. Whilst CRP is not classified as a
traditional risk factor it was included in these models because CRP is a
marker of inflammation and has also been used in other risk prediction
scores such as the Reynolds Risk Score (Ridker et al. Circulation
109:IV-6-19, 2004; Ridker et al. JAMA: Journal of the American Medical
Association 297:611-9, 2007; Shearer et al. PLoS ONE 4:e5444, 2009).

[0324] Models were validated by three-fold cross validation repeated 10
times and the results combined in a ROC analyses. In the stable CAD vs
unstable CAD models, traditional risk factors alone gave an AUC of 0.723
compared with 0.748 for 8 lipids, while the 13 traditional risk factors
combined with the 8 lipids resulted in an AUC of 0.765 (FIG. 3). In the
control vs CAD models, traditional risk factors alone gave an AUC of
0.927 compared with 0.963 for 16 lipids, while the 13 traditional risk
factors combined with the 16 lipids resulted in an AUC of 0.973 (FIG. 4).

Discussion

[0325] There are no current screening methods that can prospectively
identify unstable plaque. As proposed herein, plasma lipids are suitable
biomarkers to identify plaque instability and patient vulnerability.
ModCer, modPC and modCE lipid species were identified as useful
biomarkers that can distinguish between stable and unstable CAD. These
markers as well as previously characterised lipids enabled the creation
of a plasma lipid profile that reflected the changes in lipid metabolism
associated with the progression of CAD. In combination with the
traditional risk factors, the plasma lipid profiles improved the ability
to stratify CAD patients into stable and unstable cohorts, and may serve
as a cost effective, non-invasive clinical screening method to identify
non-symptomatic patients at risk (Damas and Aukrust Scand Cardiovasc J
40:262-6, 2006; Naghavi et al. Circulation 108:1772-8, 2003).

[0326] Identification of New Biomarkers for CAD:

[0327] Whilst the exact changes that occur in lipid metabolism during the
progression of CAD are not fully understood, there is growing evidence to
suggest that the lipid peroxidation products play a role in atherogenesis
(Davis et al. supra 2008; Oei et al. Circulation 111:570-5, 2005).
Precursor ion scanning allowed the identification of modPCs and modCer
based upon their characteristic fragmentation. The plasma concentrations
of these lipids were significantly different between the stable and
unstable CAD cohorts as well as the control and CAD cohorts. This
supports the concept that ModPCs and modCers are involved in the changes
that occur in lipid metabolism with the progression of the disease.
Whilst precursor ion scanning enabled the determination of the parent ion
m/z for these lipids, it is not able to provide information regarding
their exact structure. By identifying the species of interest (i.e. those
that show a significant difference between cohorts), this provides an
efficient means of targeting specific lipids to be further characterised
by either further mass spectrometric analysis or other structural methods
such as nuclear magnetic resonance spectroscopy. This information may
further unravel the mechanism behind the changes in lipid metabolism
driving plaque progression and instability.

Example 6

Updated Results

Updated Patient Characteristics

[0328] The patients in the stable and unstable cohorts did not differ in
conventional risk factors, with the exception of smoking status, and
hsCRP (Table 1). In contrast, most clinical and biochemical parameters
differed significantly between the control cohort and the CAD cohort
(combined stable and unstable CAD patients) (Table 1). This selection of
the control group was made to optimise the ability to identify
differentiating lipid species. Medication use was similar between the
stable and unstable groups with the exception of statin and antigoagulant
use (Table 2).

Identification of New Biomarkers and Plasma Lipid Profiling

[0329] Analysis of the plasma lipid extracts from 10 control, 10 stable
and 10 unstable CAD patients by precursor ion scanning identified 38
species of modPC, 13 species of modCer and 4 species of modCE that
displayed a significant difference between control and CAD groups. These
were combined with the other lipid species identified in plasma to define
the plasma lipid profile (Table 1, Table 7 and Table 8).

[0330] Plasma samples were analysed for 329 lipid species by two scheduled
MRM experiments. Quality control plasma samples (QC; 14 replicates) were
evenly spaced within the cohort. The coefficients of variation (CV)
within the QC samples were less than 20% for 271 of the 329 lipid
species. Those lipids which had a CV greater that 20% were primarily
lipid species that were in low abundance (<200 pmol/mL); none of these
were included in the top ranked lipid analytes used in the multivariate
models.

[0331] Binary logistic regression analysis, adjusting for age and sex
identified 30 lipids that were significantly different (p<0.01)
between the stable CAD and unstable CAD groups and 95 lipids that were
different (p<0.01) between the control and CAD (stable and unstable
combined) groups (Table 16).

Multivariate Analysis

[0332] Binary logistic regression models (3-fold cross validation repeated
100 times) were created to assess the relative contribution of lipids and
risk factors to the differentiation of stable CAD from unstable CAD and
control from CAD. Models (stable CAD vs unstable CAD) using lipids only,
traditional risk factors only or a combination of both produced
C-statistics of 0.739 (CI 0.734-0.745), 0.679 (CI 0.673-0.685) and 0.804
(CI 0.798-0.811) and % accuracy of 69.5, 64.5 and 73.3 respectively
(Table 17A). The multiple cross validation enabled us to rank the lipids
and traditional risk-factors based on their recurrent incorporation in
the logistic models. The ranked lists for the lipid only and risk factor
only models are shown in Tables 18 and 19. Table 20 shows the ranked list
for the combined lipids and traditional risk factor models. Models of
control vs CAD using lipids only, traditional risk factors only or a
combination of both produced C-statistics of 0.946 (CI 0.944-0.948),
0.956 (CI 964-0.958 and 0.982 CI 0.981-0.983 and % accuracy of 87.4, 90.3
and 92.3 respectively (Table 17B). The ranked features for the separate
lipid and risk factor models are shown in Supplementary Tables 21 and 22.
The ranked features for the combined lipids and risk factors model are
shown in Table 23.

[0333] Recursive feature elimination (RFE) analysis was also applied using
three-fold cross validation (repeated 100 times) to develop multivariate
models using support vector machine learning. Models of varying feature
size (e.g., 1, 2, 4, 8, 16 . . . , 329) that included either lipids
alone, risk factors alone or lipids with risk factors were developed. The
ranked list of the lipids/risk factors according to the frequency of
their recurrent incorporation in the generated models is shown in Tables
24 and 25. The C-statistic and % accuracy from each model was plotted
against the number of variables to assess the performance of the
different models and identify the minimum number required for optimal
discrimination (FIG. 5). Models using lipids alone (FIG. 5A circles) to
discriminate stable CAD from unstable CAD showed a maximum C-statistic
(0.739, CI 0.734-0.745) with only 16 lipids in the model. This was
significantly better than the model created with risk factors alone (FIG.
5A squares) (C-statistic of 0.679, CI 0.673-0.685), while the model
containing a combination of lipids and risk factors performed best
(C-statistic of 0.804 (CI 0.798-0.811)) with only eight features (FIG.
6). This model also had the highest accuracy of 73.3% compared to risk
factors alone (FIG. 5A triablges and FIG. 6) (64.5%) or lipids alone
(69.5%) (FIG. 5B).

[0334] Classification of CAD from control using lipids only gave a
C-statistic of 0.939 (CI 0.937-0.945) with 128 lipids in the model,
however, only 16 lipids were sufficient to give a C-statistic of 0.919
(See FIG. 5C) (CI 0.917-0.921). While the traditional risk factors
performed slightly better than lipids with a C-statistic of 0.965 (CI
0.964-0.966), the combined lipids and risk factor model performed best
with a C-statistic of 0.973 (CI 0.972-0.974) with 16 features. This model
also had the highest accuracy of 85.3% compared to risk factors (83.2%)
or lipids (80.2%) (FIG. 5D). The high level of discrimination of control
from CAD with all models reflects the CAD status of the control group
specifically chosen to highlight differences in the lipid profile between
these groups.

Updated Discussion

[0335] This study has identified differences in the plasma lipidome
between stable CAD and unstable CAD. Multivariate models combining
traditional risk factors and plasma lipids gave a significant improvement
over traditional risk factors alone such that over 73% of patients could
be correctly classified as either stable or unstable CAD. These findings
indicate that plasma lipid profiling has significant diagnostic and
prognostic potential for the identification of individuals at risk for
unstable coronary syndromes.

[0336] The healthy control group was selected to provide the greatest
phenotypic difference with the CAD groups and thereby optimise the
ability to identify new lipid markers associated with CAD. Subsequent
analyses of these new lipid markers and 276 known lipid species in the
stable and unstable CAD groups identified 30 of these lipid species as
potential biomarkers of unstable CAD. The single most prominent
difference between stable and unstable CAD was the concentration of PI
species. Total PI was 13.8% lower in the unstable CAD group relative to
the stable CAD group with 9 of the 17 species showing a significantly
lower level (p<0.01) and a further five species showing a negative
trend. This is in addition to a 13.5% decrease in the stable CAD group
relative to the control group, demonstrating an association between PI
species and disease severity. The relevance of these observations may lie
in the fact that PI, via the action of PLA2, is the primary source of
arachidonic acid which is required for the biosynthesis of the
prostaglandins and other ecosanoids that are involved in the activation
of monocytes and macrophages and associated with matrix metalloproteinase
production, a hallmark of plaque instability. PLA2 has been detected in
atherosclerotic lesions, both co-localised with macrophages and in the
extracellular matrix where it is thought to act on LDL to release
arachidonic acid.

[0337] In contrast to PI, PS which also showed a decrease in stable CAD
relative the control group (-36.1%, p=3.03E-04) displayed a higher level
in the unstable CAD relative to the stable CAD group (23.9%,
non-significant). PS is released from activated platelets in membrane
vesicles and enhances the activation of prothrombin to thrombin during
blood coagulation and thrombogenesis. However, PS is also a substrate for
a number of phospholipases which may account for the lower levels in the
stable CAD group relative to the control group.

[0338] In addition to differences between stable CAD and unstable CAD,
many lipids showed a significant difference between the control and CAD
groups. Alkylphosphatidylcholine (APC) species were almost uniformly
lower in the CAD cohort with 9 of 17 species significantly different at
the p<0.01 level and all but one species showing a negative trend.
This may relate to the higher oxidative stress in the CAD group and the
action of ROS on the polyunsaturated fatty acids of the APC species or
directly on the vinyl ether linkages of the plasmalogens, which are also
included in this lipid class. Alternatively, lower APC may be the result
of increased PLA2 activity in these patients. The primary source of PLA2
activity in circulation is the lipoprotein PLA2 (Lp-PLA2), also known as
the platelet activating factor acetylhydrolase which has been associated
with increased risk of cardiovascular disease in numerous epidemiological
studies.

[0339] However, while the action of ROS and PLA2 on these lipids would be
expected to lead to the generation of LPC, which has previously been
positively associated with inflammation and atherosclerosis, as described
herein, lower levels of all LPC species with the exception of LPC 20:4
and LPC 20:3 which were significantly higher in the CAD group. The lower
levels may result from an increase in the catabolism of these species
were observed here, but more likely relates to their more efficient
removal from circulation into tissues, either in the form of modified
low-density lipoprotein or directly from albumin, which represents the
major form of plasma LPC.

[0340] Some of these lipids (APC 34:2, LPC 16:1, LPC14:0) displayed a
further decrease in the unstable CAD relative to the stable CAD again
demonstrating an association with disease severity. LPC 14:0 had median
levels of 2038, 1619 and 1192 pmol/mL in control, stable and unstable CAD
groups respectively (FIG. 7). However, other lipids were altered
specifically in the unstable CAD group relative to the combined control
and stable CAD groups; SM 18:0 showed no difference between control and
CAD but was significantly higher in the unstable CAD group relative to
the stable CAD group (p=3.37E-3) (FIG. 7).

[0341] Differences of this type may reflect specific alterations in lipid
metabolism associated with unstable disease.

[0342] Whilst the exact changes that occur in lipid metabolism during the
progression of CAD are not fully understood, there is growing evidence to
suggest that lipid peroxidation products play a role in atherogenesis.
Precursor ion scanning allowed the identification of modified forms of PC
(modPC) that have previously been reported as oxidised and truncated
species (Davis et al., J. Biol. Chem. 283: 6428-6437, 2008; Oei et al.,
Circulation. 111: 570-575, 2005). These were also decreased in the CAD
groups relative to the control group and some species showed a further
decrease in unstable CAD relative to stable CAD. This may also be a
reflection of increased PLA2 activity and tissue uptake as oxidised PC
species are reported to be preferred substrates for LpPLA2 (Davis et al.,
2008 (supra)) and high affinity ligands for scavenger receptors. Modified
Cer species (modCer) were also identified as potential biomarkers and may
relate to the formation of acylceramide species associated with lysosomal
PLA2 activity involved in turnover of oxLDL.

[0343] Despite the incomplete knowledge of the lipid metabolism associated
with CAD lipid biomarkers are described herein as useful for the
development of multivariate models to effectively stratify individuals
based on disease status. The inventors' strategy was to incorporate lipid
classes that reflect the multiple biological functions and processes that
underlie the progression of CAD, then apply recursive feature elimination
with multiple cross validation to create optimal classification models
with the minimum number of lipids. This process demonstrated that only
8-16 lipids were required to achieve almost maximum discrimination of
disease status (FIGS. 5A and C). These lipids (Tables 24 and 25) showed a
strong homology with the top ranked lipids identified by the logistic
regression (Tables 20 and 23) as those most often incorporated into the
multivariate models, thereby supporting the RFE selection process.

[0344] The influence of statins on the plasma lipid profile was examined
in the stable CAD cohort; 9 of 229 lipids showed a correlation with
statin use (15-76% difference in concentration, p<0.01) with a further
19 having 0.01>p<0.05. However, only three of these 28 were
identified as discriminating stable CAD from unstable CAD and only six
lipid species were identified in the 95 that were statistically different
between the control and CAD groups (Table 26). Two of these (PC 37:4 and
PS 38:4) showed an opposite trend with statin use, to that observed in
the CAD group, suggesting that statin use may partially correct these
lipid levels.

[0345] Notwithstanding the limitations of a cross sectional study to
develop predictive models, many of the lipids identified as
discriminatory for unstable CAD displayed an association with disease
severity suggesting that they are altered prior to the onset of ACS. The
application of recursive feature elimination (RFE) using support vector
machine learning enabled the development and cross validation of
multivariate models for the classification of CAD patients as stable or
unstable. The combination of only eight traditional risk factors and
plasma lipids provided the best discrimination with a C-statistic of
0.804 (CI, 0.798-0.811) a significant improvement on the traditional risk
factors alone which produced a C-statistic of only 0.679 (CI,
0.673-0.685) (FIG. 6).

[0346] The Examples demonstrate the potential of plasma lipid profiling
for the identification of stable and unstable CAD.

[0347] Many modifications will be apparent to those skilled in the art
without departing from the scope of the present invention.